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1 Supplementary webappendix This webappendix formed part of the original submission and has been peer reviewed. We post it as supplied by the authors. Supplement to: Arinaminpathy N, Batra D, Khaparde S, et al. The number of privately treated tuberculosis cases in India: an estimation from drug sales data. Lancet Infect Dis 2016; published online Aug 24.
2 How many tuberculosis cases are treated outside the public health sector in India? An approach using private-sector drug sales data Appendix Sample data tables The analysis involved 189 different product codes over 22 regions in India, involving the sale of million packs of rifampicin-containing medicine in 2013 and million packs in Below we give illustrative tables for the data inputs; for demonstration, we take data relevant to Andhra Pradesh in 2013, concentrating on the ten product codes accounting for the most numbers of packs sold. Table S1 shows the total sales of each of these product codes, along with the product details (product name and number of units in a pack). The column Total packs sold provides the data for N i in equation (1) in the main text. Product code Pack Pack Size State Total packs sold Volume category AKT-4 COMBIPACK x 4 4 ANDHRA PRADESH High AKT-3 COMBIPACK x 2 2 ANDHRA PRADESH High FORECOX FILM C.TABS x 6 6 ANDHRA PRADESH High R-CINEX TABS 600 MG x 3 (/300) 3 ANDHRA PRADESH High R-CINEX CAPS 450 MG x 10 (/300) 10 ANDHRA PRADESH High MONTO-4 CAPLET KIT x 2 2 ANDHRA PRADESH High FORECOX KIT TAB-CAP.KIT x 4 4 ANDHRA PRADESH High AKURIT-4 FILM C.TABS x ANDHRA PRADESH High FORECOX-150 FILM C.TABS x 6 6 ANDHRA PRADESH High RHE-KIT TABS KIT x 2 2 ANDHRA PRADESH High Table S1: Example data for total sales of rifampicin-containing drugs in Andhra Pradesh in Shown are the 10 drugs accounting for the most numbers of packs sold (representing 72% of the total in Andhra Pradesh in 2013). For demonstration, the table also shows pack names and sizes: these are dropped in subsequent tables and in the analysis, using only the product code as a unique identifier. To estimate p i in equation (1), the proportion of sales of product i that are for TB, Table S2 shows information for the numerator and denominator. As described in the main text, these terms are used to construct a beta distribution for p i for each product. This information, available from IMS Health prescription audits, is available at the regional level (here, showing data for the South region where Andhra Pradesh belongs). 1
3 Product code Region Numerator for p i Denominator for p i South South South South South South South South South South Table S2: Data for the numbers of prescriptions involving a given product code, as captured by the IMS Health prescription audit survey. In the column headings, the numerator is the total number of prescriptions that mentioned TB as a diagnosis, and the denominator is the total number of prescriptions with any stated diagnosis. To translate packs sold to patient-days of treatment per pack (d i ), we drew again from the prescription audits. Taking the example of the top-selling pack code , Table S3 shows the frequency distribution for prescribed days of treatment per prescription, in the South region. Treatment duration 2/ / (days) Relative frequency (%) Table S3: Frequency distribution for the duration of treatment represented by each prescription of product code in the South region, as captured by the IMS Health prescription audit survey. As stated in table S2, these proportions relate to a total of TB prescriptions. The product code illustrated here represents an AKT-4 combipack supplied as four strips (see table S1), with each strip providing a single day of treatment: a full treatment course (several months of daily treatment) thus may involve a series of prescriptions for this product. Finally, estimates for c i are available only for a limited number of rifampicin-containing products. As described in the main text, to estimate c i for the remaining required products, we matched by category of sales volumes. With the column volume category in Table S1 showing categories for each rifampicin-containing product, the parameters in Table S4 show the distributions for c i associated with each category. 2
4 Volume category Mean Standard deviation Low-volume Medium-volume High-volume Table S4: Parameters of a normal distribution used to capture potential over- or under-estimates in IMS Health sales data, as captured by IMS Health validation studies. For comparison with other TB drugs, Table S5 summarizes aggregate sales data in 2014 for rifampicin, and two other drugs used to treat TB. As above, the proportion of prescriptions that are for TB is drawn from prescription audits for each of these drugs. Overall, the data illustrates that national sales volumes for different TB drugs have a scale consistent with those of rifampicin. Drugs containing: Total units sold in 2014 (million) Aevrage proportion prescribed for TB (%) Rifampicin Isoniazid Ethambutol Table S5. Summary sales and usage data for products containing three different types of TB drugs, for comparison with rifampicin. Total units denotes the sum total of drugs sold in both fixed-dose combination (FDC) and plain dosage forms. Note that Isoniazid and Ehtambutol are typically not prescribed alone, but in combination with Rifampicin. 3
5 State Patients in private sector (thousands) Patients in 3 month duration 6 month duration 9 month duration public sector (thousands) ANDHRA PRADESH 340 ( ) 170 ( ) 113 (88-147) 103 ASSAM 114 (92-152) 57 (46-76) 38 (30-50) 53 BIHAR 520 ( ) 260 ( ) 173 ( ) 66 CHHATTISGARH 100 (81-126) 50 (40-63) 33 (27-42) 25 DELHI 391 ( ) 195 ( ) 130 ( ) 50 GOA 6 (4-8) 3 (2-4) 2 (1-2) 1 GUJARAT 348 ( ) 174 ( ) 116 (93-143) 73 HARYANA 119 (96-150) 59 (48-75) 39 (32-50) 38 HIMACHAL PRADESH 16 (12-22) 8 (6-11) 5 (4-7) 13 JAMMU & KASHMIR 60 (48-80) 30 (24-40) 20 (16-26) 11 JHARKHAND 103 (88-130) 51 (44-65) 34 (29-43) 34 KARNATAKA 185 ( ) 92 (68-124) 61 (45-82) 61 KERALA 73 (56-97) 36 (28-48) 24 (18-32) 24 MADHYA PRADESH 388 ( ) 194 ( ) 129 ( ) 92 MAHARASHTRA 546 ( ) 273 ( ) 182 ( ) 135 ORISSA 41 (34-57) 20 (17-28) 13 (11-19) 45 PUNJAB 153 ( ) 76 (64-97) 51 (42-65) 39 RAJASTHAN 354 ( ) 177 ( ) 118 ( ) 90 TAMIL NADU 224 ( ) 112 (84-148) 74 (56-99) 81 UTTAR PRADESH 1647 ( ( ) 549 ( ) ) UTTARANCHAL 109 (92-148) 54 (46-74) 36 (30-49) 12 WEST BENGAL 130 ( ) 65 (52-92) 43 (35-61) 91 National 6039 ( ) 3019 ( ) 2013 ( ) Table S6. Estimated numbers of patients receiving TB treatment in Columns are as in table 2 in the main text, but showing corresponding estimates for
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